The history of neuroscience has tracked with the evolution of science and technology. Today, neuroscience's trajectory is heavily dependent on computational systems and the availability of high-performance computing (HPC), which are becoming indispensable for building simulations of the brain, coping with high computational demands of analysis of brain imaging data sets, and developing treatments for neurological diseases. This chapter will briefly review the current and potential future use of supercomputers in neuroscience.
TopComputational Neural Models
Computational models of neurons and neural networks represent one of the most essential tools that have contributed to the progress of neuroscience. For example, they are used to guide the design of experiments, to quantify relationships between anatomical and physiological data, to investigate the dynamics of systems that cannot be accessed via analytical methods, and to validate estimates made during theoretical derivations. Since the early days of neuronal simulations, a wide range of computational models have been developed ranging from models aimed at describing low-level mechanisms of neural function (e.g., molecular dynamics of ion channels in the neuron) to models of large-scale neuronal networks (Ippen, Eppler, Plesser, & Diesmann, 2017) (see (Fan & Markram, 2019) for a review).
The chosen level of abstraction for a model is based on the scientific question. If focused on sub-cellular processes (e.g., the transfer of ions underlying changes in membrane voltages that lead to APs), a neuron(s) would be described with detailed multi-compartment models (M. Hines, 1984). Instead, if the question addressed large scale network dynamics, many neurons would be described with one-compartment or few-compartment models that communicate electrically via spikes (Helias et al., 2012; Ippen et al., 2017). Simulators exist for many of these levels, including NEURON (M. L. Hines & Carnevale, 1997), SPLIT (Hammarlund & Ekeberg, 1998), PCSIM (Pecevski, Natschläger, & Schuch, 2009), the NEural Simulation Tool (NEST) (Gewaltig, 2007; van Albada, Kunkel, Morrison, & Diesmann, 2014), and C2 (Ananthanarayanan & Modha, 2007) (see (Helias et al., 2012; Tikidji-Hamburyan, Narayana, Bozkus, & El-Ghazawi, 2017) for a review).